Tips for Building a Data Science Capability

(i.e., context) and builds the institutional knowledge
and critical buy-in necessary to ensure ongoing
engagement and eventual scaling of capabilities and
solutions. These techniques refocus data efforts into
more meaningful and important questions that are
both business focused and analytically meaty.
Throughout Booz Allen Hamilton’s methodology, there
is an iterative flow of activities that deliberately
sequences divergent and convergent steps. For
instance, instead of generating ideas in a linear
fashion, where each idea is offered and then
discounted for any number of reasons, in our
approach to design thinking, ideas are generated in
large batches, built on by others, and then prioritized
based on any relevant criteria. The result is a much
larger and more fertile sandbox of opportunity. These
solution development activities help data scientists
engage with business counterparts and work quickly
and creatively toward identifying and executing the
decisions and actions necessary to realize results
with the buy-in of key business partners.
HOW BOOZ ALLEN EMBEDS DESIGN THINKING
INTO ANALYTICS
Design thinking is both an end-to-end process and a
toolbox from which to pull tools and techniques for
modular application. As such, its integration with
data science can take several forms and requires
both experienced practitioners and sufficient training
of data scientists and other stakeholders to achieve
a shared mindset and language from which to
collaborate. At Booz Allen, because we believe it is
such a powerful complement, we train our own data
scientists and our clients in these techniques so that
they can get the most organizational value possible.
BLEND DESIGN RESEARCH INTO ANALYTICS
One of the key aspects of design thinking is looking
for the hidden meaning or goals of the customer,
employee, partner, or patient, etc. It’s not enough to
identify and understand a customer’s need—organizations
need to dig deeper. Establishing a design
research capability and conducting research in
sequence with quantitative methods of research
(e.g., surveys, multivariate testing, and digital
analytics) helps to generate a more complete picture
of not just what’s happening, but why. This can
propel analytics organizations in new directions
through new levels of insight into problems that have
interactions among humans (customers, employees,
partners, etc.). The result can be a more fulfilling
analytical answer for all parties involved.
HOST COLLABORATIVE PROBLEM
REFRAMING WORKSHOPS
Booz Allen’s reframing workshops can bring together
data scientists, business owners, and even
customers (where appropriate) to explore and
discover the hidden roots of business challenges and
reframe problems into more meaningful
questions. Reframing workshops are designed to
challenge inherent assumptions made during the
analytical process, allowing the potential for breakthrough
thinking and solution development. Greater
value from data can be unleashed by following a
progressive cycle of analytical testing and reframing
to arrive at more promising (and elegant for that
matter) analytical solutions. Greater collective
understanding helps to design more insightful
research questions, and when paired with the right
analytical technique, increases the potential for
generating notable business impact.
USE STRUCTURED IDEATION TO THINK BIG
Design thinking includes many techniques for
triggering ideas, drawing on existing patterns,
solutions, and concepts and reapplying them in
novel ways. With our design thinking techniques,
ideation moves from a critical linear process of
idea-constraint-idea-constraint to a sequenced
divergent process of generating a wealth of ideas
before converging on the most promising. These
techniques allow teams to turn insights from analysis
into “so what” actions necessary to move toward
organizational value.
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